import copy
import random
from functools import partial

import numpy as np
from mmdet.cv_core.parallel import collate
from mmdet.cv_core.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader

from .samplers import GroupSampler


# only linux
# 用于查询或修改当前系统资源限制设置,可以通过代码控制进程内存,不知道纠结有多少影响？ TODO
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)  # 能打开的最大文件数
hard_limit = rlimit[1]
soft_limit = min(4096, hard_limit)
resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))


DATASETS = Registry('dataset')
PIPELINES = Registry('pipeline')


def _concat_dataset(cfg, default_args=None):
    from .dataset_wrappers import ConcatDataset
    ann_files = cfg['ann_file']
    img_prefixes = cfg.get('img_prefix', None)
    seg_prefixes = cfg.get('seg_prefix', None)
    proposal_files = cfg.get('proposal_file', None)
    separate_eval = cfg.get('separate_eval', True)

    datasets = []
    num_dset = len(ann_files)
    for i in range(num_dset):
        data_cfg = copy.deepcopy(cfg)
        # pop 'separate_eval' since it is not a valid key for common datasets.
        if 'separate_eval' in data_cfg:
            data_cfg.pop('separate_eval')
        data_cfg['ann_file'] = ann_files[i]
        if isinstance(img_prefixes, (list, tuple)):
            data_cfg['img_prefix'] = img_prefixes[i]
        if isinstance(seg_prefixes, (list, tuple)):
            data_cfg['seg_prefix'] = seg_prefixes[i]
        if isinstance(proposal_files, (list, tuple)):
            data_cfg['proposal_file'] = proposal_files[i]
        datasets.append(build_dataset(data_cfg, default_args))

    return ConcatDataset(datasets, separate_eval)


def build_dataset(cfg, default_args=None):
    from .dataset_wrappers import (RepeatDataset)
    if cfg['type'] == 'RepeatDataset':
        # 将数据集重复times次数，主要用于小数据集，否则每个epoch太短，训练时长会变长
        dataset = RepeatDataset(
            build_dataset(cfg['dataset'], default_args), cfg['times'])
    elif isinstance(cfg.get('ann_file'), (list, tuple)):
        # 多个标注文件
        dataset = _concat_dataset(cfg, default_args)
    else:
        dataset = build_from_cfg(cfg, DATASETS, default_args)

    return dataset


def build_dataloader(dataset,
                     samples_per_gpu,
                     workers_per_gpu,
                     num_gpus=1,
                     shuffle=True,
                     seed=None,
                     **kwargs):
    """Build PyTorch DataLoader.

    In distributed training, each GPU/process has a dataloader.
    In non-distributed training, there is only one dataloader for all GPUs.

    Args:
        dataset (Dataset): A PyTorch dataset.
        samples_per_gpu (int): Number of training samples on each GPU, i.e.,
            batch size of each GPU.
        workers_per_gpu (int): How many subprocesses to use for data loading
            for each GPU.
        num_gpus (int): Number of GPUs. Only used in non-distributed training.
        dist (bool): Distributed training/test or not. Default: True.
        shuffle (bool): Whether to shuffle the data at every epoch.
            Default: True.
        kwargs: any keyword argument to be used to initialize DataLoader

    Returns:
        DataLoader: A PyTorch dataloader.
    """

    sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None
    batch_size = num_gpus * samples_per_gpu
    num_workers = num_gpus * workers_per_gpu

    init_fn = partial(
        worker_init_fn, num_workers=num_workers, rank=0,
        seed=seed) if seed is not None else None

    data_loader = DataLoader(
        dataset,
        batch_size=batch_size,
        sampler=sampler,
        num_workers=num_workers,
        collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
        pin_memory=False,
        worker_init_fn=init_fn,
        **kwargs)

    return data_loader


def worker_init_fn(worker_id, num_workers, rank, seed):
    # The seed of each worker equals to
    # num_worker * rank + worker_id + user_seed
    worker_seed = num_workers * rank + worker_id + seed
    np.random.seed(worker_seed)
    random.seed(worker_seed)
